library(plotly)
library(tidyverse)
library(here)
library(readr)
library(maps)
library(dplyr)
library(ggplot2)
library(lubridate)
library(purrr) #Used to  create the animation frames
library(rmdformats)
library(heatmaply)
library(palmerpenguins)
library(reticulate)

CherryTree_Data_ <- read_csv(here("Data/CherryTree Data .csv")) # Load data

CherryTree <- as.matrix(CherryTree_Data_) # change the data set to a numeric matrix 

fig <- plot_ly(z = ~CherryTree) # plot by numeric matrix 
fig<- fig %>% add_surface()

save_image(fig, here("Output", "3DPlot.png"))

fig # Display plot

data("economics") # Load in dataset that is already built into R

econ <- economics %>% # Subset data
  mutate(cumulative_pop = cumsum(pop)) # Creates a new column

fig2 <- econ %>%
  plot_ly(x = ~date,  # Specify x and y variables of interest
          y = ~cumulative_pop, 
          fill = ~pop,
          type = "scatter", # Insert graph type
          mode = "none",
          stackgroup = "one",
          animation_frame = ~year(date)) %>% # 
    add_trace(y = ~0, 
              hoverinfo = "skip", 
              showlegend = FALSE) %>%
    layout(title = "US Population Over Time",
           xaxis = list(title = "Date"),
           yaxis = list(title = "Cumulative Population")) %>%
    colorbar(title = "Population", len = 0.5, y = 0.8, ypad = 0)

fig2

save_image(fig2, here("Output", "Econ.png"))

chemicaldata <- read_csv(here("Data/chemicaldata.csv")) # Load data

fig3 <- plot_ly(chemicaldata, type = "scattermapbox", # Specify df, graph type, and etc.
                             mode = "markers", 
                             marker = list(size = 10, 
                                           color = "red"), 
                                           lon = ~Long, lat = ~Lat)

fig3 <- fig3 %>% layout(mapbox = list(center = list(lon = -157.763, 
                                                  lat = 21.274),
                                                  zoom = 15, 
                                                  style = "open-street-map"))

fig3 # Show the map

save_image(fig3, here("Output", "ChemData.png"))

plot_ly( 
  data = penguins, 
  alpha = 0.6, 
  x = ~bill_depth_mm, 
  y = ~species, 
  type = "histogram2d", 
  text = ~paste('</br> species:', species, 
                '</br> bill depth', bill_depth_mm)) %>% 
  layout(title = "A Measure of Penguin Bill Depth and Islands", 
         xaxis = list(title = "Bill Depth (mm)"), 
         yaxis = list(title = "Species"))

penguinplot <- as.matrix(penguins_raw) # Change data set to matrix

fig4<- plot_ly(z = ~penguinplot, type = "heatmap") 

fig4

save_image(fig4, here("Output", "Heatmap.png"))

data("msleep") # Load data

sleep_bar <-msleep %>%
             plot_ly(x = ~order, 
                     y = ~sleep_rem,
                     type = "bar", 
                     name = "REM Sleep")

sleep_bar <- sleep_bar %>% 
  layout(title = "Average REM Sleep by Order", 
         xaxis = list(title = "Animal Order"), 
         yaxis = list(title = "REM Sleep (hours)"))

sleep_bar

save_image(sleep_bar, here("Output", "sleep_bar.png"))

data(msleep) # Load Data

msleep <- ggplot2::msleep %>% # we need to assign the data set "msleep" found in ggplot and assign it to our current RMarkdown.
  rename(`Type of Diet` = vore, `Total Hours of Sleep` = sleep_total) %>% #changes names before making graph so axis doesn't need to be changed later
  na.omit() #removes any incomplete or N/A data from data set

diet_sleep <- msleep %>% # We have assigned our graph the letter Q to request it easily later
  plot_ly(x = ~reorder(`Type of Diet`, -`Total Hours of Sleep`), 
          y = ~`Total Hours of Sleep`, # changing the order of how the bars show up so they're increasing in hours of sleep.
          type = 'bar', # type of graph
          marker = list(color = rainbow(nrow(msleep), #uses rainbow colors for bars indicating their sleeping hours individually and as a dietary group.
                        alpha = 0.7))) %>% #determines the transparency/opacity of the bars 0.0 being completely transparent
  
  
  layout(xaxis = list(title = "Type of Diet"), #naming x-axis
         yaxis = list(title = "Total Hours of Sleep"), #naming y-axis
         title = "Total Hours of Sleep vs. Type of Diet") #naming graph

# Add animation to the bars
diet_sleep <- diet_sleep %>% 
                animation_opts(frame = 100, easing = 'elastic') #controls timing and style of animation,

# Display the graph
diet_sleep

save_image(diet_sleep, here("Output", "diet_sleep.png"))